Mt. Everest is often referred to as the earth＇s ＇third＇ pole. As such it is relatively inaccessible and little is known about its meteorology. In 2005, an automatic weather station was operated at North Col （28...Mt. Everest is often referred to as the earth＇s ＇third＇ pole. As such it is relatively inaccessible and little is known about its meteorology. In 2005, an automatic weather station was operated at North Col （28°1′ 0.95＂ N, 86°57′ 48.4＂ E, 6523 m a.s.l.） of Mt. Everest. Based on the observational data, this paper compares the reanalysis data from NCEP/NCAR （hereafter NCEP-Ⅰ） and NCEP-DOE AMIP-Ⅱ （NCEP- Ⅱ）, in order to understand which reanalysis data are more suitable for the high Himalayas with Mr. Everest region. When comparing with those from the other levels, pressure interpolated from 500 hPa level is closer to the observation and can capture more synoptic-scale variability, which may be due to the very complex topography around Mt. Everest and the intricately complicated orographic land-atmosphereocean interactions. The interpolation from both NCEP-Ⅰ and NCEP-Ⅱ daily minimum temperature and daily mean pressure can capture most synopticscale variability （r〉0.82, n=83, p〈0.001）. However, there is difference between NCEP-Ⅰ and NCEP-Ⅱ reanalysis data because of different model parameterization. Comparing with the observation, the magnitude of variability was underestimated by 34.1%, 28.5 % and 27.1% for NCEP-Ⅰ temperature and pressure, and NCEP-Ⅱ pressure, respectively, while overestimated by 44.5 % for NCEP-Ⅱ temperature. For weather events interpolated from the reanalyzed data, NCEP-Ⅰ and NCEP-Ⅱ show the same features that weather events interpolated from pressure appear at the same day as those from the observation, and some events occur one day ahead, while most weather events and NCEP-Ⅱ temperature interpolated from NCEP-Ⅰ happen one day ahead of those from the observation, which is much important for the study on meteorology and climate changes in the region, and is very valuable from the view of improving the safety of climbers who attempt to climb Mt. Everest.展开更多
This article aims to assess the spatial distribution of the IST （internal surface temperatures） in the ceiling and DBT （dry bulb temperatures） of a LGR （light green roof） in a test cell. Cover systems known as g...This article aims to assess the spatial distribution of the IST （internal surface temperatures） in the ceiling and DBT （dry bulb temperatures） of a LGR （light green roof） in a test cell. Cover systems known as green roofs have the potential to retain rainwater and help reduce runoff. However, the characteristic considered in this work is the insulation capacity of this kind of coverage. To evaluate the spatial distribution of temperatures in an environment with light green roof, we proposed a new method for acquisition of series of climatological data and temperatures according to spatial and temporal approaches of dynamic climatology. Climatological data were provided by an automatic weather station and temperatures were collected in a test cell with light green roof. The spatial distribution of surface temperatures and internal air temperature （DBT） are based on the concepts of a climatic episode and typical experimental day from the study of the dynamic climatology. The results led to the conclusion that the light green roof has a balanced spatial distribution of the IST and of the internal air temperature （DBT）, i.e., without substantial variations over the day. The new methodology also showed the importance of specifying the location of the sensors and automatic weather station in experimental studies on the thermal behaviour of buildings.展开更多
基金funded by the National Natural Science Foundation of China (Grant No. 40501015)the Chinese Academy of Science (Grant No. KZCX3-SW-344)
文摘Mt. Everest is often referred to as the earth＇s ＇third＇ pole. As such it is relatively inaccessible and little is known about its meteorology. In 2005, an automatic weather station was operated at North Col （28°1′ 0.95＂ N, 86°57′ 48.4＂ E, 6523 m a.s.l.） of Mt. Everest. Based on the observational data, this paper compares the reanalysis data from NCEP/NCAR （hereafter NCEP-Ⅰ） and NCEP-DOE AMIP-Ⅱ （NCEP- Ⅱ）, in order to understand which reanalysis data are more suitable for the high Himalayas with Mr. Everest region. When comparing with those from the other levels, pressure interpolated from 500 hPa level is closer to the observation and can capture more synoptic-scale variability, which may be due to the very complex topography around Mt. Everest and the intricately complicated orographic land-atmosphereocean interactions. The interpolation from both NCEP-Ⅰ and NCEP-Ⅱ daily minimum temperature and daily mean pressure can capture most synopticscale variability （r〉0.82, n=83, p〈0.001）. However, there is difference between NCEP-Ⅰ and NCEP-Ⅱ reanalysis data because of different model parameterization. Comparing with the observation, the magnitude of variability was underestimated by 34.1%, 28.5 % and 27.1% for NCEP-Ⅰ temperature and pressure, and NCEP-Ⅱ pressure, respectively, while overestimated by 44.5 % for NCEP-Ⅱ temperature. For weather events interpolated from the reanalyzed data, NCEP-Ⅰ and NCEP-Ⅱ show the same features that weather events interpolated from pressure appear at the same day as those from the observation, and some events occur one day ahead, while most weather events and NCEP-Ⅱ temperature interpolated from NCEP-Ⅰ happen one day ahead of those from the observation, which is much important for the study on meteorology and climate changes in the region, and is very valuable from the view of improving the safety of climbers who attempt to climb Mt. Everest.
文摘This article aims to assess the spatial distribution of the IST （internal surface temperatures） in the ceiling and DBT （dry bulb temperatures） of a LGR （light green roof） in a test cell. Cover systems known as green roofs have the potential to retain rainwater and help reduce runoff. However, the characteristic considered in this work is the insulation capacity of this kind of coverage. To evaluate the spatial distribution of temperatures in an environment with light green roof, we proposed a new method for acquisition of series of climatological data and temperatures according to spatial and temporal approaches of dynamic climatology. Climatological data were provided by an automatic weather station and temperatures were collected in a test cell with light green roof. The spatial distribution of surface temperatures and internal air temperature （DBT） are based on the concepts of a climatic episode and typical experimental day from the study of the dynamic climatology. The results led to the conclusion that the light green roof has a balanced spatial distribution of the IST and of the internal air temperature （DBT）, i.e., without substantial variations over the day. The new methodology also showed the importance of specifying the location of the sensors and automatic weather station in experimental studies on the thermal behaviour of buildings.
